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Found 4 papers, 1 papers with code
Random projections of random manifolds
Moreover, unlike previous work, we test our theoretical bounds against numerical experiments on the actual geometric distortions that typically occur for random projections of random smooth manifolds.
Exponential expressivity in deep neural networks through transient chaos
We combine Riemannian geometry with the mean field theory of high dimensional chaos to study the nature of signal propagation in generic, deep neural networks with random weights.
A universal tradeoff between power, precision and speed in physical communication
Maximizing the speed and precision of communication while minimizing power dissipation is a fundamental engineering design goal.
A memory frontier for complex synapses
An incredible gulf separates theoretical models of synapses, often described solely by a single scalar value denoting the size of a postsynaptic potential, from the immense complexity of molecular signaling pathways underlying real synapses.
